Overview

Role: UX Researcher / Usability Tester
Timeline: Weekly assignment project
Tools: Lookback, Google Docs, spreadsheets
Methods: Task-based usability testing, concurrent think-aloud, concurrent and retrospective probing
Focus: Navigation clarity, filter usability, task efficiency, and user trust

Goal:
Evaluate the Airbnb website’s usability to identify pain points in navigation and filtering systems, measure task success, and provide data-driven recommendations to improve user experience.

The Problem

Airbnb is a widely used platform, but even established products can contain usability gaps.
Users sometimes struggle with filter complexity, hidden options, and inconsistent navigation, which can cause frustration and slow down bookings.

Problem Statement:
Users experience friction when using Airbnb’s search and filter tools, which can lead to confusion, repeated actions, and inefficiency.

Vision:
Evaluate how intuitive and effective Airbnb’s interface is, and identify opportunities to simplify the experience and increase user confidence.

Process: Usability Testing

1. Planning & Objectives

The usability test was designed to assess:

• Navigation: Is the interface intuitive and easy to follow?

• User Experience: Does the layout align with user expectations?

• Effectiveness: Can users successfully complete key booking tasks?

Testing goals:

• Measure task success rate, time on task, error rate, and navigation efficiency.

• Identify pain points and cognitive friction during typical booking scenarios.

2. Test Methods

• Concurrent Think-Aloud: Participants verbalized their thoughts while completing tasks.

• Concurrent Probing: Clarified reasoning and confusion during interaction.

• Retrospective Probing: Gathered reflections after each task.

• Task-Based Testing: Observed real user flows for searching, filtering, and selecting listings.

3. Participants

A diverse group representing Airbnb’s user base:

• Ages: 20s to 40s

• Mix of solo travelers, families, and less tech-savvy users

• Reflecting common Airbnb demographics (25–44 age group = majority of users)

Recruitment included pre-checks, consent forms, and a pilot session to refine the script and prompts.

4. Scenario & Tasks

Scenario:
“You’re planning a weekend trip and want to find a suitable Airbnb with specific amenities. Use the filters to narrow your search and complete the booking steps.”

Tasks:

1. Search for accommodations in a selected city

2. Apply filters (e.g. Wi-Fi, breakfast, free cancellation)

3. Review listing details and confirm suitability

4. Attempt to complete a booking

Metrics Collected

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Interpretation:
Users succeeded eventually but lacked confidence. They double-checked listings and struggled with poorly grouped filters.

Key Findings

1. Filters

• Hidden under unclear categories (e.g., “Breakfast” buried under amenities)

• Labels caused confusion (“Essentials” unclear)

• Users often had to restart filtering

Recommendation:

• Group filters under intuitive categories (e.g. “Dining,” “Amenities”)

• Add visual cues (icons, hover text)

• Provide persistent filter overview

2. Navigation

• Repeated back-and-forth between listings and filter menu

• Lack of key amenity previews in result cards

Recommendation:

• Show key amenities directly in search results

• Keep filters persistent across navigation

3. Cost Transparency

• Users confused by extra charges (cleaning fees) appearing late in the flow

Recommendation:

• Add upfront cost breakdown in filters

• Explain total pricing earlier to reduce surprises

Design Principles Inferred

• Clarity: Reduce cognitive load with visible, predictable filters

• Trust: Transparent cost breakdowns build confidence

• Efficiency: Preview key data to reduce unnecessary clicks

Outcomes

This usability study provided quantitative metrics and qualitative insights that pinpointed design weaknesses and user confusion.

Core result:
Airbnb’s search experience is functional but not fully intuitive.
Users can complete tasks but must rely on trial-and-error rather than clear guidance.

Impact:
Recommendations could reduce task time, errors, and cognitive load, while improving user satisfaction and trust.

Reflection

Through this project, I strengthened my ability to:

• Plan and conduct structured usability tests

• Combine quantitative metrics with qualitative insights

• Translate findings into practical design recommendations

What didn’t work:

• Some participants needed extra clarification on tasks

• Limited sample size prevented broader statistical confidence

• Testing focused on web, not mobile — future studies should expand scope

Next Steps

• Conduct mobile usability testing to compare patterns

• Run A/B tests for filter reorganization

• Validate recommendations through follow-up testing

• Explore integration of onboarding tips or guided filters for new users